About the job
As the AI Agent Manager for Commercial Operations, you will play a pivotal role in the identification, design, construction, and deployment of AI and automation agents tailored for our sales and revenue operations. This senior individual contributor position emphasizes hands-on involvement, aiming to foster grassroots adoption of AI-enhanced workflows within our commercial teams. Your mission will be to boost sales productivity, enhance pipeline quality, streamline quoting, proposal generation, and customer communications, while cultivating a culture of AI-augmented collaboration throughout the organization.
Key Responsibilities:
1. Use Case Discovery and Prioritization: Identify and assess high-value opportunities for deploying AI agents in sales operations, focusing on lead qualification, outbound sequencing, proposal drafting, quote generation, and CRM data enhancement. Prioritize projects based on effort, impact, and readiness for adoption.
2. Agent Design, Build, and Deployment: Oversee the design, configuration, and deployment of AI agents and automated workflows utilizing large language models (LLMs), APIs, and integration tools. Manage the complete lifecycle from prototype to production, including testing, monitoring, and iterative improvements.
3. Sales Productivity Improvement: Minimize manual tasks in routine commercial workflows by integrating AI agents into everyday sales processes. Measure saved time, reduced errors, and increased throughput while ensuring that agents enhance rather than replace human judgment in customer interactions.
4. Adoption and Change Management: Foster bottom-up adoption by collaborating closely with sales teams and individual contributors. Provide hands-on training, develop playbooks, conduct workshops, and cultivate internal advocates. Monitor adoption metrics and refine processes based on user feedback.
5. Data Quality and Integration: Ensure AI agents function with clean, reliable data through collaboration with revenue operations and data teams. Establish data requirements, create integration points with CRM, ERP, and communication tools, and implement feedback loops for ongoing agent accuracy enhancement.
6. Performance Measurement and Reporting: Define and monitor key performance indicators (KPIs) for each deployed agent, including adoption rates, productivity improvements, accuracy, and ROI. Report outcomes to commercial leadership and utilize data to inform scaling strategies and investment decisions.
7. Cross-Functional Collaboration: Collaborate with Product, Engineering, and IT to align agent deployments with platform capabilities, security standards, and data governance policies. Ensure adherence to approved frameworks and compliance with company policies.
8. Experimentation and Innovation: Stay abreast of emerging AI technologies, LLM developments, and industry trends to continuously innovate and improve AI capabilities within the organization.

